Digit-Serial DA-Based Fixed-Point RNNs: A Unified Approach for Enhancing Architectural Efficiency

The next crucial step in artificial intelligence involves integrating neural network models into embedded and mobile systems. This requires designing compact and energy-efficient neural network models in silicon for optimized performance. This article introduces a unified approach for enhancing the architectural efficiency of long short-term memory (LSTM) recurrent neural networks (RNNs). Precisely, two new structures (I and II) based on the two’s complement (TC) digit-serial distributed arithmetic (DSDA) technique are presented. The block-circulant matrix–vector multiplications (MVMs) and element-wise multiplications (EWMs) are formulated using TC DSDA. In addition, a fixed-point (FxP) training procedure for quantized LSTM RNNs is considered and validated for speech recognition tasks. Both structures leverage the circular rotation of weights and generate partial products with input digit slices. A new partial-product generator (PPG) and partial-product selector (PPS) designed to work with both unsigned and signed digits is introduced. In Structure I, a nonpipelined MVM is realized with a few PPGs and PPSs, followed by a shift-accumulate unit (SAU). Conversely, in Structure II, a suitably chosen depth-pipelined MVM is achieved with multiple PPGs and PPSs, followed by a shift-to-add tree (SAT). A critical path delay (CPD) analysis for both the proposed structures is also presented. Compared with previous works, post-synthesis results on 28 -nm fully depleted silicon-on-insulator (FDSOI) technology reveal that for a model size of 128 × 128 , Structures I and II provide 39.87% , 95.63% , and 30.95% , 91.18% more area and energy efficiencies, respectively.